Neuroglancer compressed_segmentation codec.
Project description
Compress Seg
Library for compressing and decompressing image segmentation (adapted from neuroglancer)
import compressed_segmentation as cseg
sx, sy, sz = (128,128,128)
dtype = np.uint64
order = 'C'
labels = np.arange(0, sx*sy*sz, dtype=dtype).reshape((sx,sy,sz), order=order)
compressed = cseg.compress(labels, order=order)
recovered = cseg.decompress(
compressed, (sx,sy,sz) dtype=dtype, order=order
)
arr = CompressedSegmentationArray(
compressed, shape=(sx,sy,sz), dtype=dtype
)
label = arr[54,32,103] # random access to single voxels w/o decompressing
uniq_labels = arr.labels() # get all distinct values w/o decompressing
binary2 = arr.remap({ 1: 2 }, preserve_missing_labels=False) # remap labels in segmentation w/o decompressing
recovered = arr.numpy() # decompress to a numpy array, same as decompress
124213 in arr # test if a value is in the array
cseg compress connectomics.npy
cseg decompress connectomics.npy.cseg --volume-size 512,512,512 --bytes 4
NOTE: This repository is the PyPI distribution repo but is based on work done by Jeremy Maitin-Shepard (Google), Stephen Plaza (Janelia Research Campus), and William Silversmith (Princeton) here: https://github.com/janelia-flyem/compressedseg
This library contains routined to decompress and compress segmentation and to manipulate compressed segmentation data defined by the neuroglancer project. compressed_segmentation essentially renumbers large bit width labels to smaller ones in chunks. This provides for large reductions in memory usage and higher compression.
Note that limitations in the compressed_segmentation format restrict the size of the chunk that can be compressed. As this limitation is data dependent, for example a random array with 1024 labels passes testing at 256x256x128, but 256x256x256 often does not.
Features
- Compression and decompression
- Random access to voxels without decompression
- Read out unique values without decompression
- Remap labels without decompression
- Command line interface for numpy files
- (TBD) Interface to relabel and manipulate segmentation from the compressed data
- C++, Python, and Go interface (see original repo for Golang)
C++ Compilation
Compiling as a shared library. Feel free to subsititute e.g. clang for the C++ compiler.
g++ -std=c++11 -O3 -fPIC -shared -I./include src/compress_segmentation.cc src/decompress_segmentation.cc -o compress_segmentation.so
Python Installation
pip
Binary Installation
$ pip install compressed-segmentation
$ python
>>> import compressed_segmentation as cseg
>>> help(cseg)
If there are pre-built binaries available for your architecture this should just work.
pip
Source Installation
If you need to build from source, you will need to have a C++ compiler installed:
$ sudo apt-get install g++ python3-dev
$ pip install numpy
$ pip install compressed-segmentation
$ python
>>> import compressed_segmentation as cseg
>>> help(cseg)
Direct Installation
Requires a C++ compiler such as g++ or clang.
Works with both Python 2 and 3. Encodes from / decodes to 3D or 4D numpy ndarrays.
$ sudo apt-get install g++ python3-dev
$ pip install -r requirements.txt
$ python setup.py install
$ python
>>> import compressed_segmentation as cseg
>>> help(cseg)
License
Please see the licenses in this repo.
Project details
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Hashes for compressed_segmentation-2.2.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 41b9728b9d7e9da907f7b376ef1ae324742e3c9ff71ffada6680f3fc44774641 |
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MD5 | 3f3de0ed65aa8c8d31214355eabd8e3b |
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BLAKE2b-256 | 53f7b23da8d07ca47d29f81216f4fca697d4b45279f3b19eb15574e3f5f59213 |
Hashes for compressed_segmentation-2.2.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | 7afdb60eb37d8d9fd7b88f5f0d96074df6f69e6eb449be295de93f99de1672fd |
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MD5 | 276a4ab856d1906c4d108b29de9b39e9 |
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BLAKE2b-256 | 884ecfbd349d596746fa24c4fadda17127d5a9b56787b6e566eb3a40bdceb849 |
Hashes for compressed_segmentation-2.2.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 79388bc7c9a560cba31e239404568984cafa0efedc5f8314127c30d24b3e3d90 |
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MD5 | 72aba9bb9b755a195ec6bbafcd6a1a65 |
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BLAKE2b-256 | 04e00a535ecb2ba1d09c8becef64a1bfe27b4965911bf2196b4fd24dfa2e7b75 |
Hashes for compressed_segmentation-2.2.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | bf06b4266d854575ad8a81d277858705234673d781a9e2dc75cffc1467311ed4 |
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MD5 | a5c7480a68ff888281878c4befe2eac7 |
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BLAKE2b-256 | 9438da9d889c3ff465823cb3e74ccb1084ed5837a51405db177d949c126ab920 |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | a2699c4c2ca28ec7f0b249f6b6edcd1b3c845f79ca65216574e6eec74236f412 |
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MD5 | d8e60d3ef297b39a0e78091c0fd1a852 |
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BLAKE2b-256 | ee270373564dee9bec883f60a72ef8e6494c6fc181532edf37b2d533b912bbf1 |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
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SHA256 | fb207d01047653becd7c1f653f66f2ff7ea2851dd2805570322624fef43627c5 |
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MD5 | bbb18ea891f5cf3d235482b97aed4574 |
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BLAKE2b-256 | 9afeceb49ec8178b370393ac969ad191dd28afbaae09135e4ce8631659c3b226 |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d7bea961720613c00896a134d8adac5c0de006544ee693042501f44fba6ad1e8 |
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MD5 | 6f0cf298c67c78897790da33d128867d |
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BLAKE2b-256 | 9e5ce3f120adcf5d175700e33140d0dcb421d040acb424c2cf833200dfebbd19 |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 25f59a321e1ed27f3c6c82fe12b18fb9e89b04b418781a7e20adbf7ef2bf9399 |
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MD5 | f9faa4a99952e86c73b54c5cc51d3e26 |
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BLAKE2b-256 | fd8680f705dc3e08029b696a548280f4f456c4a6ea9b808c5f41fca00f7d8d47 |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | cf74aa428b9a515d1ff170cc060ce2dd407174b8e30a8e610a6a309d15614dcc |
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MD5 | 9645d926c2d35e9a319b5ea2357b2595 |
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BLAKE2b-256 | 57a5d3e1a7417913f6fae1b4e52c2c03c5d929b8429ab83c710faab8d9c5d27e |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f320d6261f488482f37c8a96cef7332f34e63c759c13bd41dd8de73f3e80a88d |
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MD5 | 70ccced1281958073293095a2ca31c6f |
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BLAKE2b-256 | cb5c0e5ee8cdb8609d6e1423788d910e2e7a23bb0a280c61dae77d4938a5b73c |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7516119f50ceb8c8bd372496fab9cc2ff4c69b3a85e5bbd2d69e35c6f47b2b89 |
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MD5 | bea3796d2c2da06d05ac39bdb50a30d1 |
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BLAKE2b-256 | 7275eb3a91c303cbf8499d4724322ba9bffd660bd4b193053dc1d1dfde9f980d |
Hashes for compressed_segmentation-2.2.1-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b2e07fbfce69d1df613658f3ec8ab179f6d9c7d658be4d946405b441a788bb7d |
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MD5 | c13565e840d3f5e1f0a61e77f56f1fd8 |
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BLAKE2b-256 | a2be405cd553ba82093965e3700088f286699f34151d87b46897c1289dda14dc |